title: The identification of nonlinear biological systems: Wiener kernel approaches.
abstract: Detection, representation, and identification of nonlinearities in biological systems are considered. We begin by briefly but critically examining a well-known test of system nonlinearity, and point out that this test cannot be used to prove that a system is linear. We then concentrate on the representation of nonlinear systems by Wiener's orthogonal functional series, discussing its advantages, limitations, and biological applications. System identification through estimating the kernels in the functional series is considered in detail. An efficient time-domain method of correcting for coloring in inputs is examined and shown to result in significantly improved kernel estimates in a biologically realistic system.
authors.affiliation: Department of Electrical Engineering, Queen's University, Kingston, Ontario, Canada.
authors.forename: M J
authors.initials: MJ
authors.lastname: Korenberg
authors.forename: I W
authors.initials: IW
authors.lastname: Hunter
mesh_terms: D016014:Linear Models; D008432:Mathematical Computing; D008954:Models, Biological; D016011:Normal Distribution; D013269:Stochastic Processes